Giuseppe Casalicchio

SAILS reveals the functional forms of feature interactions in machine learning models, transforming how we interpret model behavior beyond mere detection.

Forget holdout data for feature effect estimation: training data's larger sample size usually wins, and cross-validation can further reduce model variance.

Unlock robust feature importance analysis with `xplainfi`, an R package that fills critical gaps by offering conditional importance methods and statistical inference for diverse ML models.

Questioning the common practice of interpreting data through a single model class, this work reveals the existence of alternative well-performing models across multiple model classes and their hyperparameters.

Finally, a model that rivals GA$^2$Ms in accuracy without sacrificing the interpretability of GAMs.